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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Medical Genomics

Open Access Research

Automated skin biopsy histopathological image annotation using multi-instance representation and learning

Gang Zhang13, Jian Yin1, Ziping Li2, Xiangyang Su4, Guozheng Li25 and Honglai Zhang6*

Author Affiliations

1 School of Information Science and Technology, SUN YAT-SEN University, Guangzhou, 510275, China

2 The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, 510120, China

3 School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

4 The Third Affiliated Hospital of SUN YAT-SEN University, Guangzhou, 510630, China

5 Department of Control Science and Engineering, Tongji University, Shanghai, 201804, China

6 Guangzhou University of Chinese Medicine, Guangzhou, 510120, China

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BMC Medical Genomics 2013, 6(Suppl 3):S10  doi:10.1186/1755-8794-6-S3-S10

Published: 11 November 2013

Abstract

With digitisation and the development of computer-aided diagnosis, histopathological image analysis has attracted considerable interest in recent years. In this article, we address the problem of the automated annotation of skin biopsy images, a special type of histopathological image analysis. In contrast to previous well-studied methods in histopathology, we propose a novel annotation method based on a multi-instance learning framework. The proposed framework first represents each skin biopsy image as a multi-instance sample using a graph cutting method, decomposing the image to a set of visually disjoint regions. Then, we construct two classification models using multi-instance learning algorithms, among which one provides determinate results and the other calculates a posterior probability. We evaluate the proposed annotation framework using a real dataset containing 6691 skin biopsy images, with 15 properties as target annotation terms. The results indicate that the proposed method is effective and medically acceptable.